A Superconducting Magnetic Energy Storage-Emulator/Battery ... · The superconducting magnetic...

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Abstract— This study examines the use of superconducting

magnetic and battery hybrid energy storage to compensate grid

voltage fluctuations. The superconducting magnetic energy

storage system (SMES) has been emulated by a high current

inductor to investigate a system employing both SMES and

battery energy storage experimentally. The design of the

laboratory prototype is described in detail, which consists of a

series-connected three phase voltage source inverter used to

regulate AC voltage, and two bidirectional DC/DC converters

used to control energy storage system charge and discharge. ‘DC

bus level signaling’ and ‘voltage droop control’ have been used to

automatically control power from the magnetic energy storage

system during short-duration, high power voltage sags, while the

battery is used to provide power during longer-term, low power

under-voltages.

Energy storage system hybridisation is shown to be

advantageous by reducing battery peak power demand compared

with a battery-only system, and by improving long term voltage

support capability compared with a SMES-only system.

Consequently, the SMES/battery hybrid DVR can support both

short term high-power voltage sags and long term undervoltages

with significantly reduced superconducting material cost

compared with a SMES-based system.

Index Terms-- Dynamic Voltage Restorer (DVR), Energy

Storage Integration, Sag, Superconducting Magnetic Energy

Storage, Battery.

I. INTRODUCTION

HE improvement of power quality is an important

objective for electrical utilities and industrial and

commercial consumers. Highly intermittent distributed

generation, rapidly changing loads, and direct-off-line power

electronic systems all contribute to reduced power quality

causing equipment downtime, overload and failure leading to

lost revenue [1].

Voltage disturbance is a common problem and under-

voltage conditions have been seen to occur more frequently

than overvoltage conditions [2]. Short-term under-voltage sags

Manuscript received 02/11/2015. This work was supported in by EPSRC

grant EP/K01496X/1. Dr. F. Robinson (e-mail: [email protected]) and Dr. W. Yuan

(e-mail: [email protected]) are with the Electronic and Electrical Engineering Department, University of Bath, Bath BA2 7AY, U.K.

A. Gee (e-mail: [email protected]) was with the Electronic and

Electrical Engineering Department, University of Bath, Bath BA2 7AY, U.K and is now with Department of Electrical and Electronic Engineering,

University of Bristol, Bristol, BS8 1UB, U.K.

are defined in IEEE Std. 1159-1995 [3] as a decrease to

between 0.1 and 0.9 p.u. (per unit) r.m.s voltage for durations

of 0.5 cycles to 1 min. They occur more frequently than long-

term under-voltages with significant costs to industry [4].

Long-term under-voltage events are defined as a measured

voltage less than 0.8-0.9 p.u. r.m.s voltage, lasting longer than

one minute [3] and can lead to load shedding and potentially

to voltage collapse [5]. The study below presents a means by

which both short-term and long-term voltage fluctuations can

be mitigated at the load using short-term magnetic energy

storage and long-term battery energy storage.

II. LITERATURE REVIEW

Methods to mitigate long-term voltage disturbance, such as

load disconnection [6] or modification of loads for greater

low-voltage ride-through capability may be impractical [7].

Alternatively, supply voltage can be stabilised by tap changing

transformers, uninterruptable power supplies (UPS), shunt-

connected compensators, or dynamic voltage restorer (DVR)

systems. Tap changing transformers have been shown to suffer

from a slow response time and can only output discrete

voltage levels [8]. UPS systems provide the complete voltage

waveform during a power failure and may prove costly and

unnecessary in the event of partial voltage sags. A DVR is a

series-connected device capable of voltage compensation with

fast response time by injecting a voltage in series with the

supply.

DVR systems can be self-supporting by using power from

the grid to mitigate disturbances [9]. Alternatively, DVR

systems can use energy storage to provide power during

compensation such as capacitors [10] for short-term storage or

batteries [11] for longer-term storage. Nielsen and Blaabjerg

[12] have shown that capacitor-supported DVR systems can

suffer from relatively poor performance for severe and long

duration sags. A recent study has shown that an ultra-capacitor

based DVR [13] can be used to mitigate short-term voltage

sags lasting less than one minute. Wang and Venkataramanan

[14] have shown that flywheels are a viable short-term energy

storage technology for use with voltage restorer systems both

experimentally and by simulation. Kim et al. [15] have

described a 3 MJ/750 kVA SMES-based DVR system and

shown experimental results confirming that SMES is suitable

for the compensation of short-term voltage sags. Shi et al. [16]

have used a system-level simulation to also show that SMES

A Superconducting Magnetic Energy

Storage-Emulator/Battery Supported

Dynamic Voltage Restorer

A. M. Gee, F. Robinson, Member, IEEE and W. Yuan.

T

mailto:[email protected]


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energy storage is capable of compensating voltage sags lasting

100ms.

Short-term voltage compensation alone may not be

sufficient to protect a sensitive load as both long-term [5], [17]

and short-term [2-4] voltage stability has been shown to

present a problem for many consumers. For this reason, this

study considers the use of SMES/battery hybrid energy

storage to compensate long and short-term voltage

fluctuations. Woong et al. [18] have also considered a

SMES/battery hybrid and shown it is viable for smoothing of

renewable energy generator output power and can result in

reduced energy storage system capacity and prolonged battery

life. Li et al. [19] have shown that a SMES/battery energy

storage system can improve battery lifetime in electric buses.

Deng et.al. [20] have presented a SMES/battery hybrid system

for reducing peak grid power in an electric vehicle charging

station. Nie et al. have also presented a SMES/battery hybrid

system and shown its feasibility in dealing with long term and

short term charge/discharge events in a wave energy

conversion system [21]. This study extends previous

simulation-based SMES/battery hybrid system studies [18-21]

by considering the hardware implementation of a

SMES/battery energy storage system. The design is shown to

be capable of interfacing SMES and battery energy storage

systems and controlling their power sharing to support a three

phase load, during both long-term and short-term voltage sags.

This has benefits in terms of improved long-term voltage

support capability and reduced costs compared with a purely

SMES-based system. Additional benefits include reduced

battery power rating requirement and an improvement in

expected battery life compared with a battery-only system.

III. METHODOLOGY

Fig. 1 shows the DVR system considered. The SMES has

been emulated by a 15mH, 100A inductor. During a voltage

error a three-phase inverter is used to generate the

compensation voltage at the primary of the injection

transformers (T1-T3) so that the load voltage remains close to

nominal. DC/DC converters are used to interface the battery

and SMES-emulator to the DC bus. An auxiliary supply (Aux.

Supply) is used to support the DC bus during standby

operation and charge the energy storage devices. The auxiliary

supply is disconnected and the energy storage devices provide

the necessary power for the inverter to support the load during

a voltage error.

A. DVR Control

The objective of the DVR control system is to minimise

supply voltage variations at the load terminals. This is

achieved by generating a compensating voltage at the series

injection transformer terminals. The phasor diagram in Fig.

2(a) shows various DVR voltage control techniques. ‘In phase

compensation’ causes the compensating voltage to be in phase

with the incoming supply voltage and has been shown to result

in the lowest DVR power rating [22]. ‘Pre-sag compensation’

preserves the phase of the incoming supply at the time a sag

occurs which can be beneficial in protecting loads that are

sensitive to phase disturbances. ‘Energy optimal’ control is

used to minimise DVR energy storage requirement by

injecting a voltage in quadrature to the load current. ‘In phase

compensation’ and ‘Pre-sag compensation’ have been

considered in this study. The control scheme was

implemented in the synchronous reference frame as shown in

Fig. 2(b) by converting three phase AC quantities to

equivalent two phase quantities:

sc

sb

sa

s

s

V

V

V

V

V

23

2/1

23

2/1

0

1

3

2

(1)

Figure 1. Hybrid energy storage DVR system configuration.

Figure 2. Vector diagram of DVR control strategies [1] Udvr1: ‘In

phase compensation’. Udvr2 ‘Pre-sag compensation’. Udvr3: ‘Energy optimal control’. (b) DVR control system.

Figure X1. Measured results: (a) Supply voltages (b)

Load voltage red phase (c) Load voltage red phase (d)

Load voltage blue phase.


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s

s

pllpll

pllpll

sq

sd

V

V

V

V

cossin

sincos (2)

where Vsa,b,c, Vsa,b, Vsd,q are the supply voltages and θpll is the

estimated supply phase angle.

A phase-locked loop (PLL) was used to determine the phase

angle θpll of the incoming supply based on an algorithm which

is robust in the presence of harmonics, non-symmetry and

transients [23]. Fig. 3 shows a Simulink implementation of the

PLL algorithm. This algorithm minimises the sine of the phase

error term causing the control system to be non-linear. For this

reason, the PI controller gains were tuned empirically. The

PLL algorithm requires the cosine and sine of the incoming

supply angle as inputs which can be obtained geometrically

using the orthogonal reference frame voltages from (1) as:

22)cos(

ss

s

VV

V

(3)

22)sin(

ss

s

VV

V

(4)

The PLL controller can be tuned to preserve the phase of

the incoming supply before a sag event with phase jump or,

alternatively, the PLL can be made to track the phase of the

incoming supply during a sag with phase-jump. Consequently,

by changing the PLL gains the system can be controlled to

provide ‘in phase’ or ‘pre-sag’ compensation. Fig. 4 illustrates

the results of tuning the PI controller in this way.

To detect the presence of a voltage error, the following

inequality was used [24], [25]:

thresholdqsqs,dd VVVVV 2

,2

** (5)

where Vs,d,q is the measured load voltage and V*d,q is the

desired nominal voltage in the synchronous reference frame.

Inequality (5) was also used to trigger the disconnection of the

DC bus auxilliary power supply (see Fig. 1).

The compensation voltage Vrefd,q is determined, based on the

error between the desired nominal voltage and the supply

voltage:

qdqdqrefd VVV ,,, * (6)

The PWM phase reference voltages Vrefa,b,c were generated by

transforming the required compensation voltage to the rotating

three phase reference frame:

refq

refd

pllpll

pllpll

ref

ref

V

V

V

V

cossin

sincos (7)

ref

ref

refc

refb

refa

V

Vk

V

V

V

31

31

0

31

31

32

2

3 (8)

The injection voltages, Vref_a,b,c, were multiplied by a feed-

forward constant, k to compensate for losses within the power

stage.

Sine-wave pulse width modulation (SPWM) or space vector

modulation (SVM) were considered for generating the inverter

output voltage. SVM is advantageous due to better utilisation

of the DC bus voltage and which allows deeper sag

compensation. However, SPWM allows the possibility to

mitigiate unbalanced faults so this technique was implemented

in this study. The inverter control was implemented using a

Texas Instruments F28069 32-bit micro-controller by

discretisation of the control and PLL algorithms. The inverter

system parameters are listed in the Appendix, Table AI.

Figure 3. Simulink implementation of PLL algorithm where ω0 is the fundamental output frequency in rad/s.




Figure 4. Simulated PLL Algorithm results: (a) Simulated voltage sag

with phase jump (b) Phase jump angle (c) Blue trace: supply phase

angle. Red trace: PLL output: ‘Pre-sag compensation’ with controller gains: kp = 0.5, ki = 5, (d) Blue trace: supply phase angle. Red trace: PLL

output: ‘In phase compensation’ with controller gains kp = 200, ki = 50.


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B. Energy Storage top-Level Control

The objective of the top-level energy storage control

strategy was to control the charge/discharge of each energy

storage device. A current vs. voltage active droop

characteristic was chosen as this strategy has been shown to

provide good stability and active power sharing [26]. The

converter reference currents are based solely on the level of

the DC bus voltage which is advantageous as high-bandwidth

communication between the three different converters is not

necessary. The charge or discharge priority of an energy

storage device can be adjusted raising or lowering its nominal

DC bus voltage using a technique known as “DC bus

signaling” [27]. The system was configured to prioritise the

SMES-emulator to charge/discharge before the battery. By

always prioritising the short term energy storage, battery

power cycling is reduced which can improve battery lifetime

[19].

The current droop characteristic for each device is shown in

Fig. 5, and is made up of three regions of operation. When the

DC voltage is above voltage level Vh(x) (where x refers to

energy storage system 1 or 2) or below below Vl(x),, the

converter current is limited to Imax(x) or -Imax(x). In between Vh(x)

and Vl(x), current is controlled based on the linear current vs.

voltage relationship:

xbusnomx kVVI (9)

where kx is a droop coefficient (A/V) and Ix is the energy

storage converter reference current.

C. SMES-Emulator System

To reduce costs and be able to evaluate a SMES-battery

hybrid DVR control platform, the SMES device was emulated

by using a 15mH iron-core inductor in this study. The SMES

converter was based on the asymmetric H-bridge

configuration shown in Fig. 6. This converter was rated at up

to 220A continuous current using forced air cooling. During

charge, Q2 is held ON and Q1 is modulated whereas during

discharge Q1 is held off and Q2 is modulated. The

relationship between DC bus current, inductor current and

duty ratio, is given by (12) and (13) for charge and discharge,

respectively [16].

1DII smescsmes (10)

)1( 2DII smescsmes (11)

For active current droop control according to (9), the desired

converter output current Icsmes is given by (12).

smessmesnombuscsmes kVVI _ (12)

where ksmes is the gradient of the droop controller and Vnom_smes

is the nominal voltage of the droop controller.

The required duty ratio can be determined by substituting

Eq. (10) during charge or (11) during discharge into (12) and

solving for duty ratio in real time. This allows the SMES-

inductor to be charged or discharged by simply raising or

lowering the DC bus voltage relative to the nominal Vnom_smes.

The controller was implemented using a 16-bit microcontroller

(PIC24HJ128GP502) with a switching frequency of 500Hz.

This low switching frequency was chosen to allow for extreme

(>>90% and <<10%) duty ratio operation without causing

overly narrow gate pulses. When operating at 100A nominal

current, conduction losses could be reduced significantly by

the use of low on-state resistance MOSFETs as opposed to

IGBTs in this system. This is expected not to be the case for

larger systems, operating at significantly higher nominal

currents and voltage ratings.

D. Battery System

A bidirectional synchronous-buck converter rated at 40A

output current with hysteresis current control was used to

control battery current. This topology has been previously

reported for use with interfacing an ultra-capacitor energy

storage system to the DC bus in DVR application [13].

However, the proposed system differs from previous studies

[13] in that a variable-frequency hysteresis current control has

been used. This is advantageous as it features cycle-by-cycle

current limiting, making it tolerant to short circuit faults. Also,

the proposed technique is shown below to be globally stable

over the operating range whereas typical current mode control

techniques described previously [13] require slope

compensation to ensure global stability [28]. Further

advantages include good dynamic current-tracking capability,

and robust performance despite variation and uncertainty in

operating conditions [29].

Assuming lossless, ideal components, the inductor current

in Fig. 7 is given by (13).

Figure 6. SMES DC/DC converter.




Figure 5. Energy storage systems active current droop control.





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uVuVdt

dIL batbus

L (13)

where u is a gate drive signal and Vbat and Vbus are battery and

DC bus voltages.

Sliding-mode control theory may be used to describe the

current regulation strategy [30]. The objective in this case is to

regulate the inductor IL, such that it tracks the command

reference current I*. A function σ = 0 can be defined as

follows [30]:

*IIL (14)

A switching control law which satisfies (15) such that σ and

its time derivative have opposite signs ensures the system will

converge to the state σ = 0 [30] and consequently IL = I*.

0:0 (15)

A switching control law which satisfies (15) is defined by

(16), where h is a small constant hysteresis band lying

symmetrically about the set-point value.

hifu

hifuu

,0

,1 (16)

If Vbus is greater than Vbat, then condition (15) is met, so this

becomes an operational requirement.

For active current droop control according to (9), the

following relationship can be written:

batbatnombuscbat kVVI _ (17)

where Icbat is the battery DC/DC converter reference current

and kbat is the gradient of the battery converter droop

characteristic and Vnom_bat is the nominal voltage of the battery

converter droop controller.

The average current delivered to the DC bus can then be

estimated, based on an ideal conversion ratio as follows:

bus

batcbatt

V

VII * (18)

To achieve active current droop control, the inductor current

reference I* is set as follows:

bat

busbatbatnombus

V

VkVVI _*

(19)

E. Energy Storage System Sizing

DC bus signaling has been used to control the

charge/discharge of the SMES-emulator system during short-

term voltage voltage-sags whereas the battery is used to

mitigate longer-term voltage variations. Consequently, the

sizing of the short-term and long-term energy storage systems

can be treated separately. The inductive energy storage

requirement is sized to mitigate short-term voltage sags

whereas the battery system is sized to mitigate long-term

under-voltages. The specific sag-depth and duration can be

determined by measurement [31] or by simulation. Once the

load power, depth of sag and sag duration are known, the

SMES energy-storage requirement can be determined as has

been previously reported [16]. The technique below extends

this method [16] by including the inverter losses in the

analysis.

While the DVR system is supporting the load voltage at its

nominal value, the real power provided by the DVR to the

load can be written:

loadqsagqnomq

loaddsagdnomd

sagIVV

IVVP

___

___

2

3

(20)

Assuming a sinusoidal current waveform, the losses in each

IGBT and diode in the inverter, can be estimated based on

previously reported loss models [32], [33]. Conduction loss in

each IGBT is given by:

cmcecmce

ci

IVCosM

IRCosM

P

02

82

1

38

1

(21)

where M = modulation index, Rce = IGBT on-state resistance,

Vce0 = IGBT forward voltage drop. Icm = IGBT forward current

and φ = power factor angle.

Conduction loss in each parallel diode is given by:

cmfcmf

cd

IVCosM

IRCosM

P

02

82

1

38

1

(22)

where Rf = diode on-state resistance, Vf0 = IGBT forward

voltage drop. Icm = diode current. The equivalent on-state

resistance of the IGBT (Rce) and diode (Rf) were determined

by linear interpolation of datasheet values [34]. M, φ, and Icm

can be found from the dq-axis components of the load current

and required compensation voltage.

Switching loss in the IGBT and parallel diode can be

determined as follows [32].

refdc

dccmoffcmonswi

V

VIEIEP

_

)()(1

(23)

Figure 7. Battery DC/DC converter.





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refdc

dccmrrswd

V

VIEP

_

)(1

(24)

where Vdc is the commutation voltage, Vdc_ref is the reference

voltage at which the switching loss energies are given,

Eon(Icm), Eoff(Icm) and ERR(Icm) are IGBT turn-on, turn-off and

diode reverse-recovery energies as a function of forward

current [34]. The total inverter losses can then be estimated as:

swdswicdcilossinv PPPPP 6_ (25)

If a voltage disturbance has duration Tsag, then the capacity

requirement of the energy storage systems connected to the

DC bus can be found from (20) and (25) as follows.

saglossinvsagsag TPPE _ (26)

For a given long-term under-voltage, the required discharge

power provided by the DVR can be found from (20). The

DVR energy can then be determined using the procedure

shown in (21) – (26).

F. Experimental Setup

In order to verify the performance of the proposed DVR, a

hardware prototype was constructed. The battery was a 48V,

75Ah sealed lead acid type and the SMES-emulator was

15mH/100A iron-core inductor. The nominal AC bus voltage

was set to 120Vac using a step down transformer. The load

was configured as a 1.4kW star-connected resistor.

A simple laboratory sag generator was developed using

solid-state relays to simulate faults to ground as shown in Fig.

8. System voltage measurements using isolating amplifiers

were logged together with the battery and SMES-inductor

currents using hall-effect current probes. The lead-acid battery

was maintained at a full state of charge at the beginning of

each experiment. To prevent battery overcharge, the maximum

battery charge current was limited to a low value of ~800mA

during stand-by operation. The DC bus voltage was

maintained at 160Vdc during standby operation using the

auxiliary supply.

The short-term energy storage system capacity was

estimated to support a sag to 35% of nominal voltage lasting

approximately 100ms using (26). The battery bank was sized

based on available lead acid batteries. Energy storage device

parameters are given in the Appendix, Tables AII and AIII.

The experimental set-up is shown in Figs. 8 and 9.

IV. RESULTS

Initially a three-phase voltage sag to 35% of nominal volt-

age, lasting 100ms was used to demonstrate the response of

the DVR and energy storage systems. From Fig. 10 it can be

seen that the hybrid DVR system mitigates the voltage sag

effectively during the sag event. The battery is discharged

momentarily at -1.45A at the end of the sag when the inductor

energy has been depleted.

The SMES-emulator was then removed from the system and

the test was repeated. The system response to the same three-

phase sag with only battery energy storage is shown in Fig. 11.

As the DC bus voltage falls below 140Vdc (the battery system

nominal voltage) the battery is discharged to support the DC

bus. The peak battery current is 21.13A in this case and the

DVR system can be seen to effectively mitigate the voltage

sag. Fig. 12 shows an oscilloscope trace of the DVR injection

voltage and supply voltage during a three phase sag.

To demonstrate the system performance during a long-term

under-voltage, the experimental system was configured for a

three-phase under-voltage dropping to 80% of nominal lasting

2s. The results are shown in Fig. 13. Initially, the SMES

system can be seen to support the DC bus. As the voltage error

lasts longer than the SMES-emulator can support the bus

voltage, at time = 0.4s the DC bus voltage falls to below

140Vdc and the battery energy storage system automatically

discharges.

Figure 8. Experimental Set-up.




Figure 9. Laboratory Setup.





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The battery then provides the necessary power to support

the load for the remainder of the under-voltage condition. At

Time = 2.1s the under-voltage event has finished and the DC

bus auxiliary supply reconnects. The battery and SMES-

emulator energy storage systems are then re-charged. While 2s

may not be considered a long-term under-voltage, this

duration was chosen in order to be able to see the interaction

between the energy storage systems clearly. The system

response reaches a stable operating point after approximately

0.6s which could be maintained to support the load until the

battery bank is depleted.

V. DISCUSSION

By use of the SMES-emulator hybrid system, the peak

battery current response during a three-phase sag to 35% of

nominal was reduced to 1.45A from 21.13A compared with

the battery-only system. This reduction in battery peak current

can have significant benefits in terms of improved battery life,

which is negatively affected by discharge current magnitude

[35].

From Fig. 13, it can be seen that the long-term under-

voltage performance of the system has been improved by the

introduction of battery energy storage. One of the main

shortcomings limiting the usage of SMES systems is the high

cost of the superconducting material [36]. By integrating a

battery, the hybrid DVR can be of significantly reduced cost

compared with a SMES only DVR and still be capable of

compensating a variety of short-term sags and long-term

under-voltages. In order to estimate the potential scale of the

cost reduction, the prototype system used in this study has

been considered. The SMES-inductor was emulated to give L=

15mH at 100A DC. The energy capacity of an equivalent

SMES can be calculated:

)(752

2

_ JLI

E SMESHESS (27)

Figure 10. Hybrid System Experimental results: 0.1s Three phase sag to

35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current (e) SMES-inductor current.

Figure 11. Battery System Experimental results: 0.1s Three phase sag to

35% of nominal voltage. (a) Supply voltages (b) Load voltages (c) DC Link Voltage (d) Battery Current.


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Without battery storage, the SMES needs to be able to

compensate long-term undervoltages. The nominal supply dq-

axis voltage has no quadrature component, so the direct axis

voltage Vd_nom is the peak voltage in the experimental system,

169.7V. During a sag with no phase jump, the supply voltage

falls to Vd_sag which is 80% of Vd_nom or 135.8V. Two cases of

sag duration have been considered; a sag lasting two seconds

and a sag lasting two minutes. The energy required by the

DVR inverter during the two second sag was found to be 638J

from (20)-(26), whereas the energy required to support the two

minute sag was found to be 38251J based on IGBT module

loss parameters given in [37]. The design procedure described

in [38] was then used to estimate the superconducting material

requirement assuming a working temperature of 50K. The coil

design for the different cases and overall superconducting

material cost based on the price of 2G HTS (high temperature

superconductor) of $400/kA-m [39] is given in Table 1. A

complete techno-economic analysis of the benefits of the

hybrid system is beyond the scope of this work but the

simplified calculation above shows that a significant reduction

in superconducting material cost can be achieved by

hybridisation with a battery. This would be offset by the

additional cost the battery system but worsened by the cost of

SMES refrigeration equipment which are not included in the

analysis.

VI. CONCLUSIONS

The performance a novel hybrid DVR system topology has

been assessed experimentally and shown to effectively provide

voltage compensation for short-term sags and long-term

under-voltages. A prototype system has been developed which

demonstrates an effective method of interfacing SMES and

battery energy storage systems to support a three phase load.

The system has been shown to autonomously prioritise the use

of the short-term energy storage system to support the load

during deep, short-term voltage sags and a battery for lower

depth, long-term under-voltages. This can have benefits in

terms of improved voltage support capability and reduced

costs compared with a SMES-based system. Additional

benefits include reduced battery power rating requirement and

an expected improvement in battery life compared with a

battery-only system due to reduced battery power cycling and

peak discharge power.

Figure 12. Hybrid System Experimental results: 0.14s Three phase sag to

35% of nominal voltage. Cyan trace: DVR phase-a injection voltage (100V/div). Yellow trace: Phase-a supply voltage (100V/div.). Injection transformer boost ratio: 2:1.

TABLE I

SMES PARAMETERS

Case Energy (J) Coils Turns/coil Inner Radius

(mm)

SC

Length (m)

SC

Cost ($40/m)

Hybrid System 75 1 95 40 30 $1200

SMES-

(2s) 638 6 30 55 95 $3800

SMES- (2 minute)

38251 6 200 140 200 $19000

Figure 13. Hybrid System Experimental results: Long-term three phase

undervoltage (a) RMS supply phase-voltage. (b) RMS load phase-voltage (c) DC Bus Voltage (d) Battery Current (e) SMES-inductor current.


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VII. APPENDIX

TABLE AI INVERTER SYSTEM PARAMETERS

Switching frequency 8kHz Injection transformer rating 6kVA

Injection transformer boost ratio 2:1

IGBT Modules [34] 2MBi100TA-060-50 Filter inductance 3mH

DC Bus capacitor 2 x 4700uF in series.

PLL PI gains Kp = 200 Ki = 50 Load nominal voltage 120Vac

Aux. Supply LAB-SMS 5300

Sag detect threshold: Vthreshold 0.1

TABLE AII

SMES-EMULATOR SYSTEM PARAMETERS

Switching frequency 500Hz

MOSFET (Q1, Q2) 2 in parallel in each leg. IXFN140N30P Diode (D1, D2) 2 in parallel in each leg. DSEI2X121-02A

Inductor LSMES 15mH

DC Bus capacitor 1.5mF

Droop gain 0.65 A/V

CR Snubber 3.3Ohm + 100nF

TABLE AIII

BATTERY SYSTEM PARAMETERS

Battery voltage 48V (4x12V)

Battery type Sealed lead acid 75Ah IGBT Module MG1502YS50

Power Inductor 2 x 1.8mH in parallel

Low side capacitor 4700µF DC Bus capacitor 6600µF

Droop gain 0.45 A/V

Maximum battery charge current 800mA Maximum battery discharge current 32A

TABLE AIV

EXPERIMENTAL SYSTEM PARAMETERS

Load 1.4kW resistive

Line impedance 5 , resistive

Step-down transformer ratio 2:1 Three phase sag fault impedance 3 resistive

Single phase under-voltage fault impedance 25 resistive

ADC card NI USB-6463 Sample rate 10kHz

VIII. ACKNOWLEDGMENT

The author would like to thank financial support from

EPSRC EP/K01496X/1 and support from the Royal Academy

of Engineering's Research Fellowship programme.

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Anthony Gee received his BSc degree in Cybernetics and Control Engineering from the

University of Reading, England. He then completed

a PhD in supercapacitor/battery hybrid energy

storage systems at the University of Bath. Since

then he has worked with Williams Advanced

Engineering and is currently working at the Energy Management Group, University of Bristol as a

power electronics hardware design engineer. His

research interests are in power electronic converters, motor drives and renewable energy systems.

Francis Robinson (M’1988) gained a PhD from

Heriot-Watt University, Edinburgh, Scotland. He

has been a lecturer at the Department of Electronic and Electrical Engineering, University of Bath since

1990, and is a member of the Electromagnetic,

Machines and Drives Group. His teaching and research are primarily related to power electronics

and drives. He has previously worked as a power

electronics design engineer with Posidata (Dana)

LTD, Rutherford Appleton Laboratory, and GEC Small Machines Ltd. Dr

Robinson is a chartered engineer in the UK and a member of the IET and

IEEE.

Weijia Yuan received his Bachelor degree from Tsinghua University and his PhD from the

University of Cambridge. He then became both a

research associate in the Engineering Department and a junior research fellow at Wolfson College,

both at the University of Cambridge. Dr Yuan

joined the University of Bath as a lecturer (US equivalent: Assistant Professor) in 2011. Dr Yuan’s

research interests focus on electrical power

applications of superconductivity including fault current limiters, machines and power transmission

lines. He has extensive research experience in modelling of superconducting

devices and building superconducting magnets.

http://www.americas.fujielectric.com/sites/default/files/2MBI150U2A-060.pdf

http://www.americas.fujielectric.com/sites/default/files/2MBI150U2A-060.pdf



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